code: def _rgb_metrics(images, images_pred, masks, masks_pred, masks_crop):
      assert masks_crop is not None
      if images.shape[1] != images_pred.shape[1]:
          raise ValueError(
              f"Network output's RGB images had {images_pred.shape[1]} "
              f"channels. {images.shape[1]} expected."
          )
      rgb_squared = ((images_pred - images) ** 2).mean(
          dim=1, keepdim=True
      )
      rgb_loss = utils.huber(rgb_squared, scaling=0.03)
      crop_mass = masks_crop.sum().clamp(1.0)
      results = {
          "rgb_huber": (rgb_loss * masks_crop).sum() / crop_mass,
          "rgb_mse": (rgb_squared * masks_crop).sum() / crop_mass,
          "rgb_psnr": utils.calc_psnr(
              images_pred, images, mask=masks_crop
          ),
      }
      if masks is not None:
          masks = masks_crop * masks
          results["rgb_psnr_fg"] = utils.calc_psnr(
              images_pred, images, mask=masks
          )
          results["rgb_mse_fg"] = (
              rgb_squared * masks
          ).sum() / masks.sum().clamp(1.0)
      return results
  
imports: import warnings
  from typing import Any, Dict, Optional
  import torch
  from pytorch3d.implicitron.models.renderer.ray_sampler import ImplicitronRayBundle
  from pytorch3d.implicitron.tools import metric_utils
  from pytorch3d.implicitron.tools.config import registry, ReplaceableBase
  from pytorch3d.ops import padded_to_packed
  from pytorch3d.renderer import utils
  from .renderer.base import RendererOutput
label_desc: Use image node_id 1 for calling a global function with example usage: _rgb_metrics(images, images_pred, masks, masks_pred, masks_crop) and returns: results